Mathematical models to characterize early epidemic growth: A review

Chowell, Gerardo; Sattenspiel, Lisa; Bansal, Shweta; Viboud, Cécile

doi:10.1016/j.plrev.2016.07.005

Cited by 403 publications

(425 citation statements)

References 111 publications

(166 reference statements)

Supporting

Mentioning

407

Contrasting

Unclassified

Order By: Relevance

“…Our results emphasize the need to consider sub-exponential growth patterns in the design and parameterization of mathematical transmission models [14]. Indeed, past modeling efforts have incorporated mechanisms to account for slower than exponential growth patterns including models that gradually mitigate the transmission rate over time [15] or incorporate phenomenological parameters to capture non-homogeneous population mixing [16,17] and models with particular spatial structuring [18–26].…”

Section: Discussionmentioning

confidence: 90%

“…Indeed, past modeling efforts have incorporated mechanisms to account for slower than exponential growth patterns including models that gradually mitigate the transmission rate over time [15] or incorporate phenomenological parameters to capture non-homogeneous population mixing [16,17] and models with particular spatial structuring [18–26]. A recent review article [14] surveys mathematical modeling approaches that are useful for capturing a diversity of early epidemic growth profiles, ranging from sub-exponential to exponential growth. These include models incorporating spatial details or realistic population mixing structures, including meta-population models, individual-based network models, and simple SIR-type models that incorporate the effects of reactive behavior changes or inhomogeneous mixing [14].…”

Section: Discussionmentioning

confidence: 99%

“…A recent review article [14] surveys mathematical modeling approaches that are useful for capturing a diversity of early epidemic growth profiles, ranging from sub-exponential to exponential growth. These include models incorporating spatial details or realistic population mixing structures, including meta-population models, individual-based network models, and simple SIR-type models that incorporate the effects of reactive behavior changes or inhomogeneous mixing [14]. …”

Section: Discussionmentioning

confidence: 99%

See 2 more Smart Citations

Is it growing exponentially fast? – Impact of assuming exponential growth for characterizing and forecasting epidemics with initial near-exponential growth dynamics

Chowell

Viboud

2016

Infectious Disease Modelling

Self Cite

View full text Add to dashboard Cite

The increasing use of mathematical models for epidemic forecasting has highlighted the importance of designing models that capture the baseline transmission characteristics in order to generate reliable epidemic forecasts. Improved models for epidemic forecasting could be achieved by identifying signature features of epidemic growth, which could inform the design of models of disease spread and reveal important characteristics of the transmission process. In particular, it is often taken for granted that the early growth phase of different growth processes in nature follow early exponential growth dynamics. In the context of infectious disease spread, this assumption is often convenient to describe a transmission process with mass action kinetics using differential equations and generate analytic expressions and estimates of the reproduction number. In this article, we carry out a simulation study to illustrate the impact of incorrectly assuming an exponential-growth model to characterize the early phase (e.g., 3–5 disease generation intervals) of an infectious disease outbreak that follows near-exponential growth dynamics. Specifically, we assess the impact on: 1) goodness of fit, 2) bias on the growth parameter, and 3) the impact on short-term epidemic forecasts. Our findings indicate that devising transmission models and statistical approaches that more flexibly capture the profile of epidemic growth could lead to enhanced model fit, improved estimates of key transmission parameters, and more realistic epidemic forecasts.

show abstract

Section: Discussionmentioning

confidence: 90%

Section: Discussionmentioning

confidence: 99%

Section: Discussionmentioning

confidence: 99%

See 1 more Smart Citation

Is it growing exponentially fast? – Impact of assuming exponential growth for characterizing and forecasting epidemics with initial near-exponential growth dynamics

Chowell

Viboud

2016

Infectious Disease Modelling

Self Cite

View full text Add to dashboard Cite

show abstract

“…The critical epidemic thresholds and the infection spreading pattern using meanfield approximation (MFA) and results obtained through extensive numerical simulations are researched in [1,2]. A new susceptible-infected-susceptible (SIS) epidemic model incorporated with multistage infection (infection delay) and an infective medium (propagation vector) over complex networks [3][4][5][6]. In [7][8][9], a novel ISIR epidemic model with nonlinear forces of infection to characterize the epidemic spread on social contact networks with the consideration of the "crowding" or "protection effect" is proposed.…”

Section: Background and Statusmentioning

confidence: 99%

Online Social Network Emergency Public Event Information Propagation and Nonlinear Mathematical Modeling

Liu¹,

Liu²,

Zeng³

2017

Complexity

View full text Add to dashboard Cite

Emergency public event arises everyday on social network. The information propagation of emergency public event (favorable and harmful) is researched. The dynamics of a susceptible-infected-susceptible and susceptible-infected-removed epidemic models incorporated with information propagation of emergency public event are studied. In particular, we investigate the propagation model and the infection spreading pattern using nonlinear dynamic method and results obtained through extensive numerical simulations. We further generalize the model for any arbitrary number of infective network nodes to mimic existing scenarios in online social network. The simulation results reveal that the inclusion of multiple infective node achieved stability and equilibrium in the proposed information propagation model.

show abstract

“…

…”

mentioning

confidence: 93%

Early sub-exponential epidemic growth: Simple models, nonlinear incidence rates, and additional mechanisms

Chowell

Sattenspiel

Bansal

et al. 2016

Physics of Life Reviews

Self Cite

View full text Add to dashboard Cite

We would like to thank all of the commentators for their insightful and positive reactions to our review paper [1]. Their comments touch on both theoretical and applied aspects of sub-exponential growth dynamics and the mechanisms that generate them, and have greatly enhanced and broadened the discussion. Here we aim to further discuss key points raised by Brauer [2], Danon and Brooks-Pollock [3], Allen [4], Merler [5], Champredon and Earn [6], and House [7].Brauer [2] underscores the flexibility of the generalized-growth model to capture the early transmission dynamics of infectious disease outbreaks, particularly in situations where retrospective investigations are not sufficient to elucidate the underlying mechanisms. We agree with this assessment. In real epidemic settings, gaining a complete understanding of the actual mechanisms that shape early epidemic growth could be particularly challenging in the absence of additional data to characterize contact patterns, population behavior changes, and transmission pathways (e.g., hospital vs. community transmission). This is further complicated when the epidemiology or transmission mechanisms of the infectious disease in question have not been fully elucidated. Related to this point, Danon and Brooks-Pollock [3] argue for the application of data science approaches to further our understanding of the processes that shape epidemic outbreaks, which, in turn could lead to improved predictive models. We could not agree more with this suggestion. Mathematical epidemiology has made important strides in recent years precisely because new and better data sources are becoming available. These data sources include electronic records containing information about the health of individuals such as primary care visits, hospitalizations, and deaths. In addition, DOI of original article: http://dx.

show abstract

Mathematical models to characterize early epidemic growth: A review

Cited by 403 publications

References 111 publications

Is it growing exponentially fast? – Impact of assuming exponential growth for characterizing and forecasting epidemics with initial near-exponential growth dynamics

Is it growing exponentially fast? – Impact of assuming exponential growth for characterizing and forecasting epidemics with initial near-exponential growth dynamics

Online Social Network Emergency Public Event Information Propagation and Nonlinear Mathematical Modeling

Early sub-exponential epidemic growth: Simple models, nonlinear incidence rates, and additional mechanisms

Contact Info

Product

Resources

About